Hany Hossny , January 6, 2023

Data Science Delivery Practices

Machine Learning vs Traditional Modelling/Programming Data science is the process of building models that analyze historical data and learn the pattern bridging the input and the output in a way that can be repeated using future data coming as input and predicting...

dataworks     7 minutes
Hany Hossny , January 6, 2023

50 Data Science Interview Questions

I needed this list of questions when I was interviewing for data science or ML engineering roles. This list aim to help data science leaders interview DS/ML engineers and help DS/ML engineers to study what is important and ace their interviews. What is the...

dataworks     3 minutes
Hany Hossny , January 6, 2023

Machine Learning Runtime Challenges (Model-Ops)

Once the ML model is deployed to production, many issues occur in runtime reducing the predictive accuracy and shortening the lifetime. Without monitoring, the model can easily hit any of the runtime bombs and fail due to data drift, concept drift, anomalies, etc....

dataworks     4 minutes
Hany Hossny , January 4, 2023
MLOPs makes ML development continuous in a never-ending loop

MLOps vs DevOps: Why is MLOps different? (MLOps-2)

ML operations aim to accelerate, scale and sustain model development, deployment and maintenace. MLOps is a typical Dev-Ops process, which is the sequence of actions taking an ML project from development to production, except MLOPs have a different nature than...

dataworks     3 minutes
Hany Hossny , January 4, 2023

What is ML-Ops? Why is it important?

Businesses around the globe use machine learning to predict their sales, profits, costs, performance, and use these predictions to build actionable insights. These insights help decision-makers to take the right decision according to the current situation after...

Hany Hossny , December 24, 2022

Data Science Process: From Business to Delivery

The Data Science Process   Data science projects start with business needs and go through data analysis, model development, deployment to production, quality assurance, and after-deployment support. The image below illustrates the key steps and actions...

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